Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	39.0	56.815357
daal4py_KNeighborsClassifier	0.0	2.0	32.864518
KNeighborsClassifier_kd_tree	0.0	2.0	27.430972
daal4py_KNeighborsClassifier_kd_tree	0.0	0.0	27.479858
KMeans_tall	0.0	0.0	21.353145
daal4py_KMeans_tall	0.0	0.0	7.817818
KMeans_short	0.0	0.0	2.871868
daal4py_KMeans_short	0.0	0.0	1.389684
LogisticRegression	0.0	0.0	18.777409
daal4py_LogisticRegression	0.0	0.0	3.860825
Ridge	0.0	0.0	10.258869
daal4py_Ridge	0.0	0.0	1.876589
HistGradientBoostingClassifier	0.0	5.0	0.255292
lightgbm	0.0	5.0	7.348520
xgboost	0.0	7.0	57.898713
catboost	0.0	5.0	10.135635
total	1.0	9.0	48.552901

KNeighborsClassifier vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	brute

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.477	0.000	1.677	0.000	-1	100	NaN	NaN	0.458	0.000	1.042	0.000	See	See
1	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.557	0.000	0.000	0.033	-1	100	0.940	0.736	1.810	0.043	17.987	0.423	See	See
2	KNeighborsClassifier	predict	1000000	1	100	NaN	0.161	0.011	0.000	0.161	-1	100	0.000	1.000	0.080	0.001	2.025	0.134	See	See
3	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.635	0.000	-1	5	NaN	NaN	0.441	0.000	0.273	0.000	See	See
4	KNeighborsClassifier	predict	1000000	1000	100	NaN	32.232	0.000	0.000	0.032	-1	5	0.806	0.828	1.797	0.019	17.937	0.194	See	See
5	KNeighborsClassifier	predict	1000000	1	100	NaN	0.163	0.013	0.000	0.163	-1	5	1.000	1.000	0.078	0.001	2.081	0.173	See	See
6	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.121	0.000	6.612	0.000	1	100	NaN	NaN	0.441	0.000	0.274	0.000	See	See
7	KNeighborsClassifier	predict	1000000	1000	100	NaN	21.065	0.090	0.000	0.021	1	100	0.940	0.934	1.886	0.047	11.167	0.280	See	See
8	KNeighborsClassifier	predict	1000000	1	100	NaN	0.182	0.000	0.000	0.182	1	100	0.000	1.000	0.084	0.010	2.166	0.265	See	See
9	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.119	0.000	6.749	0.000	1	1	NaN	NaN	0.444	0.000	0.267	0.000	See	See
10	KNeighborsClassifier	predict	1000000	1000	100	NaN	11.987	0.038	0.000	0.012	1	1	0.722	0.736	1.800	0.022	6.660	0.082	See	See
11	KNeighborsClassifier	predict	1000000	1	100	NaN	0.170	0.001	0.000	0.170	1	1	1.000	1.000	0.080	0.001	2.130	0.030	See	See
12	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.117	0.000	6.854	0.000	-1	1	NaN	NaN	0.443	0.000	0.264	0.000	See	See
13	KNeighborsClassifier	predict	1000000	1000	100	NaN	22.888	0.167	0.000	0.023	-1	1	0.722	0.934	1.847	0.028	12.392	0.207	See	See
14	KNeighborsClassifier	predict	1000000	1	100	NaN	0.155	0.018	0.000	0.155	-1	1	1.000	1.000	0.079	0.001	1.967	0.226	See	See
15	KNeighborsClassifier	fit	1000000	1000000	100	NaN	0.117	0.000	6.845	0.000	1	5	NaN	NaN	0.439	0.000	0.266	0.000	See	See
16	KNeighborsClassifier	predict	1000000	1000	100	NaN	20.836	0.048	0.000	0.021	1	5	0.806	0.828	1.855	0.047	11.234	0.283	See	See
17	KNeighborsClassifier	predict	1000000	1	100	NaN	0.181	0.002	0.000	0.181	1	5	1.000	1.000	0.084	0.008	2.162	0.197	See	See
18	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.055	0.000	0.293	0.000	-1	100	NaN	NaN	0.099	0.000	0.549	0.000	See	See
19	KNeighborsClassifier	predict	1000000	1000	2	NaN	29.473	0.067	0.000	0.029	-1	100	0.986	0.979	0.289	0.007	102.110	2.476	See	See
20	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.001	0.000	0.021	-1	100	1.000	1.000	0.006	0.000	3.632	0.224	See	See
21	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.056	0.000	0.288	0.000	-1	5	NaN	NaN	0.101	0.000	0.551	0.000	See	See
22	KNeighborsClassifier	predict	1000000	1000	2	NaN	29.471	0.082	0.000	0.029	-1	5	0.983	0.992	0.291	0.004	101.240	1.555	See	See
23	KNeighborsClassifier	predict	1000000	1	2	NaN	0.021	0.003	0.000	0.021	-1	5	1.000	1.000	0.005	0.000	3.884	0.509	See	See
24	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.053	0.000	0.300	0.000	1	100	NaN	NaN	0.103	0.000	0.520	0.000	See	See
25	KNeighborsClassifier	predict	1000000	1000	2	NaN	18.837	0.040	0.000	0.019	1	100	0.986	0.988	0.330	0.008	57.138	1.332	See	See
26	KNeighborsClassifier	predict	1000000	1	2	NaN	0.017	0.000	0.000	0.017	1	100	1.000	1.000	0.006	0.000	3.064	0.146	See	See
27	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.294	0.000	1	1	NaN	NaN	0.099	0.000	0.552	0.000	See	See
28	KNeighborsClassifier	predict	1000000	1000	2	NaN	9.342	0.019	0.000	0.009	1	1	0.974	0.979	0.277	0.004	33.699	0.442	See	See
29	KNeighborsClassifier	predict	1000000	1	2	NaN	0.014	0.000	0.000	0.014	1	1	1.000	1.000	0.005	0.000	2.538	0.162	See	See
30	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.298	0.000	-1	1	NaN	NaN	0.098	0.000	0.546	0.000	See	See
31	KNeighborsClassifier	predict	1000000	1000	2	NaN	20.177	0.114	0.000	0.020	-1	1	0.974	0.988	0.331	0.006	60.875	1.073	See	See
32	KNeighborsClassifier	predict	1000000	1	2	NaN	0.018	0.003	0.000	0.018	-1	1	1.000	1.000	0.005	0.000	3.368	0.493	See	See
33	KNeighborsClassifier	fit	1000000	1000000	2	NaN	0.054	0.000	0.298	0.000	1	5	NaN	NaN	0.099	0.000	0.542	0.000	See	See
34	KNeighborsClassifier	predict	1000000	1000	2	NaN	18.750	0.029	0.000	0.019	1	5	0.983	0.992	0.274	0.006	68.415	1.536	See	See
35	KNeighborsClassifier	predict	1000000	1	2	NaN	0.016	0.000	0.000	0.016	1	5	1.000	1.000	0.005	0.000	3.232	0.187	See	See

KNeighborsClassifier_kd_tree vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	kd_tree

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.046	0.000	0.026	0.000	1	100	NaN	NaN	0.696	0.000	4.379	0.000	See	See
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	4.368	0.038	0.000	0.004	1	100	0.977	0.981	0.560	0.007	7.800	0.124	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.004	0.001	0.000	0.004	1	100	1.000	1.000	0.001	0.000	4.552	2.076	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.022	0.000	0.026	0.000	-1	5	NaN	NaN	0.705	0.000	4.286	0.000	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.759	0.013	0.000	0.001	-1	5	0.978	0.977	0.180	0.004	4.215	0.111	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.001	0.000	0.003	-1	5	1.000	1.000	0.000	0.000	8.845	3.437	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.999	0.000	0.027	0.000	1	1	NaN	NaN	0.677	0.000	4.434	0.000	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.654	0.006	0.000	0.001	1	1	0.969	0.963	0.097	0.002	6.769	0.127	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	4.915	2.438	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.985	0.000	0.027	0.000	1	5	NaN	NaN	0.674	0.000	4.427	0.000	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	1.289	0.019	0.000	0.001	1	5	0.978	0.981	0.549	0.007	2.349	0.047	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.002	0.000	0.000	0.002	1	5	1.000	1.000	0.001	0.000	1.852	0.789	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	3.069	0.000	0.026	0.000	-1	100	NaN	NaN	0.685	0.000	4.482	0.000	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	2.537	0.017	0.000	0.003	-1	100	0.977	0.977	0.181	0.003	13.991	0.238	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.012	0.019	0.000	0.012	-1	100	1.000	1.000	0.000	0.000	34.993	56.103	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	NaN	2.849	0.000	0.028	0.000	-1	1	NaN	NaN	0.694	0.000	4.108	0.000	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	NaN	0.400	0.005	0.000	0.000	-1	1	0.969	0.963	0.099	0.003	4.064	0.138	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	NaN	0.003	0.000	0.000	0.003	-1	1	1.000	1.000	0.000	0.000	11.173	5.486	See	See
18	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.767	0.000	0.021	0.000	1	100	NaN	NaN	0.465	0.000	1.650	0.000	See	See
19	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.053	0.000	0.000	0.000	1	100	0.984	0.980	0.006	0.001	8.214	0.759	See	See
20	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	100	1.000	1.000	0.000	0.000	5.060	3.555	See	See
21	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.761	0.000	0.021	0.000	-1	5	NaN	NaN	0.451	0.000	1.688	0.000	See	See
22	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.001	0.001	0.000	-1	5	0.984	0.981	0.001	0.000	24.785	6.744	See	See
23	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	5	1.000	1.000	0.000	0.000	18.394	13.148	See	See
24	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.742	0.000	0.022	0.000	1	1	NaN	NaN	0.462	0.000	1.605	0.000	See	See
25	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.023	0.000	0.001	0.000	1	1	0.977	0.974	0.001	0.000	32.163	10.046	See	See
26	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	1	1.000	1.000	0.000	0.000	5.355	3.878	See	See
27	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.773	0.000	0.021	0.000	1	5	NaN	NaN	0.459	0.000	1.684	0.000	See	See
28	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.026	0.000	0.001	0.000	1	5	0.984	0.980	0.006	0.000	4.105	0.257	See	See
29	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.001	0.000	0.000	0.001	1	5	1.000	1.000	0.000	0.000	4.804	3.777	See	See
30	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.812	0.000	0.020	0.000	-1	100	NaN	NaN	0.453	0.000	1.795	0.000	See	See
31	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.045	0.004	0.000	0.000	-1	100	0.984	0.981	0.001	0.000	41.695	10.852	See	See
32	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	100	1.000	1.000	0.000	0.000	19.033	13.150	See	See
33	KNeighborsClassifier_kd_tree	fit	1000000	1000000	2	NaN	0.773	0.000	0.021	0.000	-1	1	NaN	NaN	0.456	0.000	1.698	0.000	See	See
34	KNeighborsClassifier_kd_tree	predict	1000000	1000	2	NaN	0.025	0.000	0.001	0.000	-1	1	0.977	0.974	0.001	0.000	35.325	9.483	See	See
35	KNeighborsClassifier_kd_tree	predict	1000000	1	2	NaN	0.002	0.000	0.000	0.002	-1	1	1.000	1.000	0.000	0.000	19.310	13.729	See	See

KMeans_tall vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_tall	fit	1000000	1000000	2	30	0.613	0.0	0.783	0.000	k-means++	NaN	30	NaN	0.408	0.0	1.503	0.000	See	See
1	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.383	0.000	k-means++	0.001	30	0.000	0.000	0.0	8.037	4.300	See	See
2	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	k-means++	1.000	30	1.000	0.000	0.0	10.714	7.606	See	See
3	KMeans_tall	fit	1000000	1000000	2	30	0.479	0.0	1.002	0.000	random	NaN	30	NaN	0.431	0.0	1.112	0.000	See	See
4	KMeans_tall	predict	1000000	1000	2	30	0.001	0.0	0.387	0.000	random	0.001	30	0.000	0.000	0.0	8.172	4.892	See	See
5	KMeans_tall	predict	1000000	1	2	30	0.001	0.0	0.000	0.001	random	1.000	30	1.000	0.000	0.0	10.403	8.190	See	See
6	KMeans_tall	fit	1000000	1000000	100	30	6.175	0.0	3.887	0.000	k-means++	NaN	30	NaN	2.593	0.0	2.382	0.000	See	See
7	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.792	0.000	k-means++	0.002	30	0.002	0.000	0.0	5.725	2.402	See	See
8	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	k-means++	1.000	30	1.000	0.000	0.0	9.148	5.344	See	See
9	KMeans_tall	fit	1000000	1000000	100	30	5.665	0.0	4.236	0.000	random	NaN	30	NaN	2.773	0.0	2.043	0.000	See	See
10	KMeans_tall	predict	1000000	1000	100	30	0.002	0.0	15.096	0.000	random	0.001	30	0.002	0.000	0.0	5.680	2.562	See	See
11	KMeans_tall	predict	1000000	1	100	30	0.001	0.0	0.020	0.001	random	1.000	30	1.000	0.000	0.0	8.798	4.787	See	See

KMeans_short vs daal4py

All estimators share the following hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	20
n_init	1
tol	0.0

	estimator	function	n_samples_train	n_samples	n_features	n_iter_sklearn	mean_sklearn	stdev_sklearn	throughput	latency	init	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	KMeans_short	fit	10000	10000	2	20	0.235	0.0	0.014	0.000	k-means++	NaN	20	NaN	0.034	0.0	6.923	0.000	See	See
1	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.189	0.000	k-means++	0.001	20	0.002	0.001	0.0	2.609	0.488	See	See
2	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.0	10.375	7.157	See	See
3	KMeans_short	fit	10000	10000	2	20	0.080	0.0	0.040	0.000	random	NaN	20	NaN	0.087	0.0	0.921	0.000	See	See
4	KMeans_short	predict	10000	1000	2	20	0.002	0.0	0.189	0.000	random	-0.000	20	0.001	0.001	0.0	2.535	0.445	See	See
5	KMeans_short	predict	10000	1	2	20	0.001	0.0	0.000	0.001	random	1.000	20	1.000	0.000	0.0	9.919	6.370	See	See
6	KMeans_short	fit	10000	10000	100	20	0.641	0.0	0.250	0.000	k-means++	NaN	20	NaN	0.137	0.0	4.693	0.000	See	See
7	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.531	0.000	k-means++	0.316	20	0.269	0.001	0.0	2.079	0.280	See	See
8	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	k-means++	1.000	20	1.000	0.000	0.0	8.022	4.298	See	See
9	KMeans_short	fit	10000	10000	100	20	0.212	0.0	0.756	0.000	random	NaN	20	NaN	0.331	0.0	0.640	0.000	See	See
10	KMeans_short	predict	10000	1000	100	20	0.002	0.0	6.548	0.000	random	0.322	20	0.311	0.001	0.0	2.208	0.323	See	See
11	KMeans_short	predict	10000	1	100	20	0.001	0.0	0.012	0.001	random	1.000	20	1.000	0.000	0.0	7.605	3.593	See	See

LogisticRegression vs daal4py

All estimators share the following hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	LogisticRegression	fit	1000000	1000000	100	[20]	10.424	0.0	[-0.11318398]	0.000	NaN	NaN	NaN	NaN	NaN	1.767	0.000	5.898	0.000	See	See
1	LogisticRegression	predict	1000000	1000	100	[20]	0.000	0.0	[51.41401393]	0.000	NaN	NaN	NaN	NaN	0.536	0.000	0.000	0.924	0.426	See	See
2	LogisticRegression	predict	1000000	1	100	[20]	0.000	0.0	[0.20775902]	0.000	NaN	NaN	NaN	NaN	1.000	0.000	0.000	0.465	0.419	See	See
3	LogisticRegression	fit	1000	1000	10000	[27]	0.766	0.0	[-2.78891705]	0.001	NaN	NaN	NaN	NaN	NaN	0.746	0.000	1.026	0.000	See	See
4	LogisticRegression	predict	1000	100	10000	[27]	0.002	0.0	[133.37056183]	0.000	NaN	NaN	NaN	NaN	0.230	0.003	0.001	0.521	0.245	See	See
5	LogisticRegression	predict	1000	1	10000	[27]	0.000	0.0	[22.70510277]	0.000	NaN	NaN	NaN	NaN	0.000	0.001	0.000	0.136	0.086	See	See

Ridge vs daal4py

All estimators share the following hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	deprecated
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

	estimator	function	n_samples_train	n_samples	n_features	n_iter	mean_sklearn	stdev_sklearn	throughput	latency	max_iter	random_state	r2_score	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	sklearn_profiling	daal4py_profiling
0	Ridge	fit	1000	1000	10000	NaN	0.183	0.0	0.437	0.0	NaN	NaN	NaN	0.177	0.000	1.038	0.000	See	See
1	Ridge	predict	1000	1000	10000	NaN	0.010	0.0	8.132	0.0	NaN	NaN	0.122	0.017	0.002	0.575	0.064	See	See
2	Ridge	predict	1000	1	10000	NaN	0.000	0.0	1.013	0.0	NaN	NaN	NaN	0.000	0.000	0.726	0.678	See	See
3	Ridge	fit	1000000	1000000	100	NaN	1.325	0.0	0.604	0.0	NaN	NaN	NaN	0.226	0.000	5.855	0.000	See	See
4	Ridge	predict	1000000	1000	100	NaN	0.000	0.0	3.590	0.0	NaN	NaN	1.000	0.000	0.000	0.980	1.414	See	See
5	Ridge	predict	1000000	1	100	NaN	0.000	0.0	0.014	0.0	NaN	NaN	NaN	0.000	0.000	0.675	0.758	See	See

Environment information

{
  "system_info": {
    "python": "3.8.10 | packaged by conda-forge | (default, May 11 2021, 07:01:05)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1047-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1.2",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "1.0.dev0",
    "numpy": "1.20.3",
    "scipy": "1.6.3",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": "3.4.2",
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.15.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.15",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/python3.8/site-packages/scikit_learn.libs/libgomp-f7e03b3e.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}